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Related Experiment Videos

Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment

C E Lawrence1, S F Altschul, M S Boguski

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894.

Science (New York, N.Y.)
|October 8, 1993
PubMed
Summary
This summary is machine-generated.

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Detecting subtle sequence patterns across multiple DNA and protein sequences is challenging. A new iterative sampling algorithm efficiently finds these local multiple alignments, revealing shared molecular structures and biological properties.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Genome projects generate vast amounts of protein and DNA sequence data.
  • Deciphering these sequences and their relationships is hindered by the difficulty of detecting subtle, shared local residue patterns.
  • These patterns often indicate conserved molecular structures and biological functions.

Purpose of the Study:

  • To develop a sensitive, computer-automated algorithm for detecting subtle local residue patterns common to multiple sequences.
  • To address the challenge of understanding relationships within large biological sequence datasets.

Main Methods:

  • Developed a novel algorithm based on the statistical method of iterative sampling.
  • Mathematically defined the "local multiple alignment" problem for full computer automation.

Related Experiment Videos

  • The algorithm operates in N-linear time, processing N sequences efficiently.

    • Main Results:

    • The algorithm achieves optimized local alignment models for N sequences.
    • It requires only seconds on current workstations for analysis.
    • Enables simultaneous detection and optimization of multiple patterns and their repeats.

    Conclusions:

    • The new iterative sampling algorithm provides a sensitive and efficient solution for local multiple sequence alignment.
    • This method facilitates the discovery of conserved patterns, aiding in the understanding of molecular structures and biological properties across diverse protein families.
    • Demonstrated applicability to helix-turn-helix proteins, lipocalins, and prenyltransferases.